metadata

tags:
  - merge
  - mergekit
  - lazymergekit
  - FelixChao/WestSeverus-7B-DPO-v2
  - mayflowergmbh/Wiedervereinigung-7b-dpo
  - cognitivecomputations/openchat-3.5-0106-laser
base_model:
  - FelixChao/WestSeverus-7B-DPO-v2
  - mayflowergmbh/Wiedervereinigung-7b-dpo
  - cognitivecomputations/openchat-3.5-0106-laser

Spaetzle-v61-7b

Spaetzle-v61-7b is a merge of the following models using LazyMergekit:

🧩 Configuration

models:
  - model: LeoLM/leo-mistral-hessianai-7b-chat
    # no parameters necessary for base model
  - model: FelixChao/WestSeverus-7B-DPO-v2
    parameters:
      density: 0.60
      weight: 0.30
  - model: mayflowergmbh/Wiedervereinigung-7b-dpo
    parameters:
      density: 0.65
      weight: 0.40
  - model: cognitivecomputations/openchat-3.5-0106-laser
    parameters:
      density: 0.6
      weight: 0.3
merge_method: dare_ties
base_model: LeoLM/leo-mistral-hessianai-7b-chat
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0
tokenizer_source: base

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "cstr/Spaetzle-v61-7b"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])